scholarly journals Extending the Cultivation Area of Pecan (Carya illinoinensis) Toward the South in Southeastern Subtropical China May Cause Increased Cold Damage

2021 ◽  
Vol 12 ◽  
Author(s):  
Jinbin Zheng ◽  
Heikki Hänninen ◽  
Jianhong Lin ◽  
Sitian Shen ◽  
Rui Zhang
Keyword(s):  
Large Scale ◽  
Model Development ◽  
Carya Illinoinensis ◽  
Climatic Warming ◽  
Subtropical China ◽  
Southeastern China ◽  
Subtropical Zone ◽  
Cold Damage ◽  
Biological Realism ◽  
Subsequent Risk

Pecan (Carya illinoinensis) is an important nut tree species in its native areas in temperate and subtropical North America, and as an introduced crop in subtropical southeastern China as well. We used process-based modeling to assess the effects of climatic warming in southeastern China on the leaf-out phenology of pecan seedlings and the subsequent risk of “false springs,” i.e., damage caused by low temperatures occurring as a result of prematurely leafing out. In order to maximize the biological realism of the model used in scenario simulations, we developed the model on the basis of experiments explicitly designed for determining the responses modeled. The model showed reasonable internal accuracy when calibrated against leaf-out observations in a whole-tree chamber (WTC) experiment with nine different natural-like fluctuating temperature treatments. The model was used to project the timing of leaf-out in the period 2022–2099 under the warming scenarios RCP4.5 and RCP8.5 in southeastern China. Two locations in the main pecan cultivation area in the northern subtropical zone and one location south of the main cultivation area were addressed. Generally, an advancing trend of leaf-out was projected for all the three locations under both warming scenarios, but in the southern location, a delay was projected under RCP8.5 in many years during the first decades of the 21st century. In the two northern locations, cold damage caused by false springs was projected to occur once in 15–26 years at most, suggesting that pecan cultivation can be continued relatively safely in these two locations. Paradoxically, more frequent cold damage was projected for the southern location than for the two northern locations. The results for the southern location also differed from those for the northern locations in that more frequent cold damage was projected under the RCP4.5 warming scenario (once in 6 years) than under the RCP8.5 scenario (once in 11 years) in the southern location. Due to the uncertainties of the model applied, our conclusions need to be re-examined in an additional experimental study and further model development based on it; but on the basis of our present results, we do not recommend starting large-scale pecan cultivation in locations south of the present main pecan cultivation area in southeastern subtropical China.

scholarly journals Effects of climatic warming on spring phenology in subtropical trees: process-based modelling with experiments designed for model development

2020 ◽  
Author(s):  
Rui Zhang ◽  
Jianhong Lin ◽  
Fucheng Wang ◽  
Heikki Hänninen ◽  
Jiasheng Wu
Keyword(s):  
Model Development ◽  
Climatic Warming ◽  
Spring Phenology ◽  
Rest Break ◽  
Tree Phenology ◽  
Adult Trees ◽  
Climatic Scenarios ◽  
Torreya Grandis ◽  
Female Flowering ◽  
Biological Realism

AbstractTo project the effects of climatic warming on the timing of spring leafout and flowering in trees, process-based tree phenology models are often used nowadays. Unfortunately, the biological realism of the models is often compromised because the model development has often been based on various assumptions and indirect methods. We developed process-based tree phenology models for four subtropical tree species, and for the first time for these trees, we based the model development on explicit experimental work particularly designed to address the processes being modelled. For all the four species, a model of seedling leafout was developed, and for Torreya grandis, a model for female flowering in adult trees was additionally developed. The models generally showed reasonable accuracy when tested against two sources of independent data: observational phenological records and leafout data from a whole-tree chamber warming experiment. In scenario simulations, the models projected an advanced spring phenology under climatic warming for 2020 – 2100. For the leafout of seedlings, the advancing rates varied between 4.7 and 5.9 days per one °C warming, with no major differences found between the climatic scenarios RCP4.5 and RCP8.5. For Torreya flowering, less advancing was projected, and the projected advancing per one °C warming was less for RCP8.5 (0.9 days / °C) than for RCP4.5 (2.3 days / °C). The low advancing rates of Torreya flowering were caused by reduced chilling under the warming climate and by the particular temperature responses found for Torreya flowering. For instance, our results show that in Torreya flower buds, no rest break (endodormancy release) is seen at +15 °C, whereas in the seedlings of all four species, +15 °C has a clear rest-breaking effect. These findings highlight the need to base the model development on explicit experiments particularly designed to address the process being modelled.

scholarly journals ENSO modulates wildfire activity in China

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Keyan Fang ◽  
Qichao Yao ◽  
Zhengtang Guo ◽  
Ben Zheng ◽  
Jianhua Du ◽  
...  
Keyword(s):  
Continuous Monitoring ◽  
Southern Oscillation ◽  
Fire Suppression ◽  
Winter Season ◽  
Plant Tissues ◽  
Fire Occurrence ◽  
Subtropical China ◽  
Southeastern China ◽  
Temperature Ranges ◽  
Low Precipitation

AbstractChina is a key region for understanding fire activity and the drivers of its variability under strict fire suppression policies. Here, we present a detailed fire occurrence dataset for China, the Wildfire Atlas of China (WFAC; 2005–2018), based on continuous monitoring from multiple satellites and calibrated against field observations. We find that wildfires across China mostly occur in the winter season from January to April and those fire occurrences generally show a decreasing trend after reaching a peak in 2007. Most wildfires (84%) occur in subtropical China, with two distinct clusters in its southwestern and southeastern parts. In southeastern China, wildfires are mainly promoted by low precipitation and high diurnal temperature ranges, the combination of which dries out plant tissue and fuel. In southwestern China, wildfires are mainly promoted by warm conditions that enhance evaporation from litter and dormant plant tissues. We further find a fire occurrence dipole between southwestern and southeastern China that is modulated by the El Niño-Southern Oscillation (ENSO).

scholarly journals Hydrological recovery in two large forested watersheds of southeastern China: the importance of watershed properties in determining hydrological responses to reforestation

2016 ◽  
Vol 20 (12) ◽  
pp. 4747-4756 ◽  
Author(s):  
Wenfei Liu ◽  
Xiaohua Wei ◽  
Qiang Li ◽  
Houbao Fan ◽  
Honglang Duan ◽  
...  
Keyword(s):  
Watershed Management ◽  
Large Scale ◽  
Management Strategies ◽  
Forest Recovery ◽  
Southeastern China ◽  
Forested Watersheds ◽  
Positive Effects ◽  
Low Flows ◽  
Hydrological Responses ◽  
High Flows

Abstract. Understanding hydrological responses to reforestation is an important subject in watershed management, particularly in large forested watersheds ( >  1000 km2). In this study, we selected two large forested watersheds (Pingjiang and Xiangshui) located in the upper reach of the Poyang Lake watershed, southeastern China (with an area of 3261.4 and 1458 km2, respectively), along with long-term data on climate and hydrology (1954–2006) to assess the effects of large-scale reforestation on streamflow. Both watersheds have similar climate and experienced comparable and dramatic forest changes during the past decades, but with different watershed properties (e.g., the topography is much steeper in Xiangshui than in Pingjiang), which provides us with a unique opportunity to compare the differences in hydrological recovery in two contrasted watersheds. Streamflow at different percentiles (e.g., 5, 10, 50 and 95 %) were compared using a combination of statistical analysis with a year-wise method for each watershed. The results showed that forest recovery had no significant effects on median flows (Q50%) in both watersheds. However, reforestation significantly reduced high flows in Pingjiang, but had limited influence in Xiangshui. Similarly, reforestation had significant and positive effects on low flows (Q95%) in Pingjiang, while it did not significantly change low flows in Xiangshui. Thus, hydrological recovery is limited and slower in the steeper Xiangshui watershed, highlighting that watershed properties are also important for determining hydrological responses to reforestation. This finding has important implications for designing reforestation and watershed management strategies in the context of hydrological recovery.

scholarly journals Machine learning-based feature importance approach for sensitivity analysis of steel frames

2021 ◽  
Author(s):  
Hyeyoung Koh ◽  
Hannah Beth Blum
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Feature Selection ◽  
Large Scale ◽  
Failure Modes ◽  
Model Development ◽  
Predictive Performance ◽  
Computational Effort ◽  
Structural Systems ◽  
Feature Importance

This study presents a machine learning-based approach for sensitivity analysis to examine how parameters affect a given structural response while accounting for uncertainty. Reliability-based sensitivity analysis involves repeated evaluations of the performance function incorporating uncertainties to estimate the influence of a model parameter, which can lead to prohibitive computational costs. This challenge is exacerbated for large-scale engineering problems which often carry a large quantity of uncertain parameters. The proposed approach is based on feature selection algorithms that rank feature importance and remove redundant predictors during model development which improve model generality and training performance by focusing only on the significant features. The approach allows performing sensitivity analysis of structural systems by providing feature rankings with reduced computational effort. The proposed approach is demonstrated with two designs of a two-bay, two-story planar steel frame with different failure modes: inelastic instability of a single member and progressive yielding. The feature variables in the data are uncertainties including material yield strength, Young’s modulus, frame sway imperfection, and residual stress. The Monte Carlo sampling method is utilized to generate random realizations of the frames from published distributions of the feature parameters, and the response variable is the frame ultimate strength obtained from finite element analyses. Decision trees are trained to identify important features. Feature rankings are derived by four feature selection techniques including impurity-based, permutation, SHAP, and Spearman's correlation. Predictive performance of the model including the important features are discussed using the evaluation metric for imbalanced datasets, Matthews correlation coefficient. Finally, the results are compared with those from reliability-based sensitivity analysis on the same example frames to show the validity of the feature selection approach. As the proposed machine learning-based approach produces the same results as the reliability-based sensitivity analysis with improved computational efficiency and accuracy, it could be extended to other structural systems.

scholarly journals M. cerebralis establishment and spread: A graphical synthesis

2021 ◽  
Author(s):  
Pouria Ramazi ◽  
Samuel Matthias Fischer ◽  
Julie Alexander ◽  
Clayton James ◽  
Andrew J. Paul ◽  
...  
Keyword(s):  
Large Scale ◽  
Graphical Model ◽  
Empirical Studies ◽  
Model Development ◽  
Ecological Impacts ◽  
Surveillance Program ◽  
Whirling Disease ◽  
Spread Model ◽  
Considerable Impact ◽  
Economic Constraints

M. cerebralis is the parasite causing whirling disease, which has dramatic ecological impacts due to its potential to cause high mortality in salmonids. The large-scale efforts, necessary to underpin an effective surveillance program, have practical and economic constraints. There is, hence, a clear need for models that can predict the parasite spread. Model development, however, often heavily depends on knowing influential variables and governing mechanisms. We have developed a graphical model for the establishment and spread of M. cerebralis by synthesizing experts’ opinion and empirical studies. First, we conducted a series of workshops with experts to identify variables believed to impact the establishment and spread of the parasite M. cerebralis and visualized their interactions via a directed acyclic graph. Then we refined the graph by incorporating empirical findings from the literature. The final graph’s nodes correspond to variables whose considerable impact on M. cerebralis establishment and spread is either supported by empirical data or confirmed by experts, and the graph’s directed edges represent direct causality or strong correlation. This graphical model facilitates communication and education of whirling disease and provides an empirically driven framework for constructing future models, especially Bayesian networks.

scholarly journals Automated Compartment Model Development Based on Data from Flow-Following Sensor Devices

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1651
Author(s):  
Jonas Bisgaard ◽  
Tannaz Tajsoleiman ◽  
Monica Muldbak ◽  
Thomas Rydal ◽  
Tue Rasmussen ◽  
...  
Keyword(s):  
Large Scale ◽  
Model Development ◽  
Axial Flow ◽  
Compartment Model ◽  
Mixing Times ◽  
Flow Rates ◽  
Stirred Vessel ◽  
Compartment Models ◽  
Flow Models ◽  
Sensor Devices

Due to the heterogeneous nature of large-scale fermentation processes they cannot be modelled as ideally mixed reactors, and therefore flow models are necessary to accurately represent the processes. Computational fluid dynamics (CFD) is used more and more to derive flow fields for the modelling of bioprocesses, but the computational demands associated with simulation of multiphase systems with biokinetics still limits their wide applicability. Hence, a demand for simpler flow models persists. In this study, an approach to develop data-based flow models in the form of compartment models is presented, which utilizes axial-flow rates obtained from flow-following sensor devices in combination with a proposed procedure for automatic zoning of volume. The approach requires little experimental effort and eliminates the necessity for computational determination of inter-compartmental flow rates and manual zoning. The concept has been demonstrated in a 580 L stirred vessel, of which models have been developed for two types of impellers with varying agitation intensities. The sensor device measurements were corroborated by CFD simulations, and the performance of the developed compartment models was evaluated by comparing predicted mixing times with experimentally determined mixing times. The data-based compartment models predicted the mixing times for all examined conditions with relative errors in the range of 3–27%. The deviations were ascribed to limitations in the flow-following behavior of the sensor devices, whose sizes were relatively large compared to the examined system. The approach provides a versatile and automated flow modelling platform which can be applied to large-scale bioreactors.

Enterprise Modeling: Practices and Perspectives

1995 ◽  
Author(s):  
Lars C. Christensen ◽  
Brage W. Johansen ◽  
Nils Midjo ◽  
Jan Onarheim ◽  
Tor G. Syvertsen ◽  
...  
Keyword(s):  
Large Scale ◽  
Model Development ◽  
Enterprise Modeling ◽  
Organizational Restructuring ◽  
Coordination Environment ◽  
Process Improvements ◽  
Modeling Technology ◽  
Process Plant ◽  
Enterprise Model ◽  
Oil Company

Abstract This paper presents an overview of various approaches to enterprise modeling, illustrated by present and future applications of enterprise modeling technology. A taxonomy derived from different objectives of enterprise modeling is proposed. Preliminary experiences from a large-scale enterprise modeling and organizational restructuring project are reported. The project was conducted at a natural gas process plant operated by the Norwegian oil company Statoil. We argue that the potential of enterprise modeling in business process improvements only can be utilized when the methodology is brought to the heads and hands of the inhabitants of the enterprise. Finally, a coordination environment denoted “the control room metaphor” is presented as a futuristic view of enterprise model development and application.

scholarly journals Demand Response Model Development for Smart Households Using Time of Use Tariffs and Optimal Control—The Isle of Wight Energy Autonomous Community Case Study

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 541 ◽  
Author(s):  
Sourav Khanna ◽  
Victor Becerra ◽  
Adib Allahham ◽  
Damian Giaouris ◽  
Jamie M. Foster ◽  
...  
Keyword(s):  
Demand Response ◽  
Large Scale ◽  
Energy Transport ◽  
Model Development ◽  
Software Tool ◽  
Load Flow ◽  
Smart Devices ◽  
Smart Meters ◽  
Isle Of Wight

Residential variable energy price schemes can be made more effective with the use of a demand response (DR) strategy along with smart appliances. Using DR, the electricity bill of participating customers/households can be minimised, while pursuing other aims such as demand-shifting and maximising consumption of locally generated renewable-electricity. In this article, a two-stage optimization method is used to implement a price-based implicit DR scheme. The model considers a range of novel smart devices/technologies/schemes, connected to smart-meters and a local DR-Controller. A case study with various decarbonisation scenarios is used to analyse the effects of deploying the proposed DR-scheme in households located in the west area of the Isle of Wight (Southern United Kingdom). There are approximately 15,000 households, of which 3000 are not connected to the gas-network. Using a distribution network model along with a load flow software-tool, the secondary voltages and apparent-power through transformers at the relevant substations are computed. The results show that in summer, participating households could export up to 6.4 MW of power, which is 10% of installed large-scale photovoltaics (PV) capacity on the island. Average carbon dioxide equivalent (CO2e) reductions of 7.1 ktons/annum and a reduction in combined energy/transport fuel-bills of 60%/annum could be achieved by participating households.

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